1 credit
Fall 2025 Lecture Distance Learning
This course provides a control theory and data science approach to traditional epidemic models. Traditional epidemiological ideas will be explored and combined with probability theory and systems theoretic ideas to be able to capture spread behavior, learn from data, and design mitigation techniques. Namely, the course consists of four modules: 1) Virus Models, 2) Limiting Behavior, 3) Parameter Identification, and 4) Mitigation Algorithms. Prerequisite: ECE 60281.
Learning Outcomes1Differentiate between distinct compartmental models for epidemics (SI, SIS, SIR, etc.) and identify the best model for a given scenario.
2Analyze the limiting behavior of models for epidemic processes by identifying the different possible equilibria of the models and specifying conditions for converging to different equilibria.
3Estimate model parameters from data for the different epidemic models.
4Employ the estimated model parameters to forecast the impact of an outbreak.
5Choose the best model for a given scenario/dataset by employing their knowledge of the epidemic models and by comparing the fit from the estimated parameters and the forecast accuracy.
6Develop and implement mitigation algorithms for the different models of epidemic processes.
Restrictions
made by Purdue students.